Nelson Rodrigues

23 papers C 6Journal 2Unranked 13
YearRankTypeTitle / Venue / Authors
2025 C conf
INDIN
Ali Abbasi, João Luís Sobral, Nelson Rodrigues
2023 conf
CSCN
Diana Magalhães, Vinicius C. Ferreira, Nelson Rodrigues, João M. Fernandes
2020 conf
ISIE
Nelson Rodrigues, José Lima, Pedro João Rodrigues, José Augusto Carvalho, Jorge Laranjeira, Wellington Maidana, Paulo Leitão
2019 conf
ICPS
Paulo Leitão, Nelson Rodrigues, Adriano Ferreira, Arnaldo Pagani, Pierluigi Petrali, José Barbosa
2019
Nelson Rodrigues
2019 conf
ICPS
David Costa, Flávia Pires, Nelson Rodrigues, José Barbosa, Getúlio Igrejas, Paulo Leitão
2018 J jnl
Comput. Ind.
Nelson Rodrigues, Eugénio C. Oliveira, Paulo Leitão
2017 conf
HoloMAS
Nelson Rodrigues, Paulo Leitão, Eugénio C. Oliveira
2016 conf
SOHOMA
Nelson Rodrigues, Paulo Leitão, Eugénio C. Oliveira
2015 conf
MATES
Nelson Rodrigues, Paulo Leitão, Eugénio C. Oliveira
2015 C conf
INDIN
Adriano Ferreira, Arnaldo Pereira, Nelson Rodrigues, José Barbosa, Paulo Leitão
2015 J jnl
IEEE Trans. Ind. Informatics
Paulo Leitão, Nelson Rodrigues, Claudio Turrin, Arnaldo Pagani
2015 ch.
Service Orientation in Holonic and Multi-agent Manufacturing
Nelson Rodrigues, Paulo Leitão, Eugénio C. Oliveira
2015 C conf
ETFA
Paulo Leitão, Nelson Rodrigues, José Barbosa
2014 conf
DoCEIS
Nelson Rodrigues, Eugénio C. Oliveira, Paulo Leitão
2013 conf
ISIE
Nelson Rodrigues, Paulo Leitão, Matthias Foehr, Claudio Turrin, Arnaldo Pagani, Roberto Decesari
2013 conf
HoloMAS
Nelson Rodrigues, Arnaldo Pereira, Paulo Leitão
2013 conf
ISIE
Arnaldo Pereira, Nelson Rodrigues, José Barbosa, Paulo Leitão
2012 C conf
ETFA
Arnaldo Pereira, Nelson Rodrigues, Paulo Leitão
2012 C conf
IECON
Paulo Leitão, Nelson Rodrigues, Claudio Turrin, Arnaldo Pagani, Pierluigi Petrali
2012 conf
ISIE
Paulo Leitão, Nelson Rodrigues
2012 C conf
IECON
Lorenzo Stroppa, Nelson Rodrigues, Paulo Leitão, Nicola Paone
2011 conf
HoloMAS
Paulo Leitão, Nelson Rodrigues
redb/extractors/js_extractors/js_suspicious_apis.py
← Index redb/extractors/js_extractors/js_suspicious_apis.py python
import inspect
from datetime import datetime, timezone
from typing import Any

from redb.extractors.enum import Tag
from redb.extractors.js_extractor import JSExtractor
from redb.extractors.js_extractors.js_patterns import CATEGORIES, PATTERNS


# Backwards-compatible export: `{category: [(raw_pattern_string, api_name), ...]}`
# in canonical PATTERNS insertion order (code_execution, network, filesystem,
# process, registry, crypto_encoding, dom_manipulation). Kept so external
# callers (notably JSDeobfuscationExtractor pre-cleanup) keep working until
# they are migrated to PATTERNS directly.
SUSPICIOUS_APIS: "dict[str, list[tuple[str, str]]]" = {}
for _name, _compiled in PATTERNS.items():
    SUSPICIOUS_APIS.setdefault(CATEGORIES[_name], []).append((_compiled.pattern, _name))


class JSSuspiciousAPIsExtractor(JSExtractor):

    def __init__(
        self, filepath, log, exporters=None, index_prefix=None,
        known_benign=False, known_malicious=False, source=None, context=None,
    ):
        super().__init__(
            filepath, log, exporters, index_prefix,
            known_benign, known_malicious, source, context=context,
        )
        self.api_findings = None
        self.log.debug(inspect.currentframe().f_code.co_name)

    def tag(self):
        return Tag.JS_SUSPICIOUS_APIS.value

    def _get_context_snippet(self, line, max_len=200):
        """Get a truncated context snippet around a match."""
        line = line.strip()
        if len(line) > max_len:
            return line[:max_len] + "..."
        return line

    def extract(self):
        src = self.js_source
        if not src:
            return None

        # Pass 1: shared per-sample scan over the raw source. The dict contains
        # entries for both PATTERNS and FEATURE_PATTERNS; the loop below only
        # consults PATTERNS keys, so feature-only entries are ignored.
        raw_scan = self._context.scan or {}
        raw_lines = self.lines

        # Pass 2: same patterns over the deobfuscated text, when the
        # deobfuscator produced something meaningfully different. APIs hidden
        # behind one obfuscation layer (Vjw0rm-style array.join + eval,
        # Dean-Edwards packers, jjencode, ...) only surface here. The scan is
        # cached on JSContext so JSDeobfuscationExtractor (which computes the
        # new_apis_found diff) reuses the same result.
        deobf_scan = self._context.scan_deobfuscated
        if deobf_scan:
            deobf_text, _ = self._context.deobfuscated
            deobf_lines = deobf_text.splitlines()
        else:
            deobf_lines = []

        findings = []
        # Iterate PATTERNS in canonical order so output is deterministic and
        # matches the historical category/pattern ordering. For each api_name,
        # raw findings take precedence; if an API is found only in the
        # deobfuscated text, we surface it as a row tagged revealed_by_deobf=1
        # with line numbers / snippets pulled from the deobfuscated source.
        for api_name in PATTERNS:
            raw_info = raw_scan.get(api_name)
            if raw_info:
                line_numbers = raw_info["lines"]
                lines_for_snippets = raw_lines
                revealed_by_deobf = 0
            else:
                deobf_info = deobf_scan.get(api_name)
                if not deobf_info:
                    continue
                line_numbers = deobf_info["lines"]
                lines_for_snippets = deobf_lines
                revealed_by_deobf = 1

            snippets = [
                self._get_context_snippet(lines_for_snippets[ln - 1])
                for ln in line_numbers[:3]
                if 0 < ln <= len(lines_for_snippets)
            ]
            findings.append({
                "api_name": api_name,
                "api_category": CATEGORIES[api_name],
                # Historical semantics: count = number of unique lines with a
                # match, not total in-source match count.
                "call_count": len(line_numbers),
                "line_numbers": line_numbers,
                "context_snippet": " | ".join(snippets),
                "revealed_by_deobf": revealed_by_deobf,
            })

        if not findings:
            return None

        self.api_findings = findings
        return findings

    def prepare_export_data(self, exporter_type: str) -> Any:
        if exporter_type == "ClickHouseExporter":
            if not self.api_findings:
                return None

            current_time = datetime.now(timezone.utc)
            data = []
            for f in self.api_findings:
                data.append([
                    self.sha256,
                    f['api_name'],
                    f['api_category'],
                    f['call_count'],
                    f['line_numbers'],
                    f['context_snippet'],
                    f['revealed_by_deobf'],
                    current_time,
                ])

            column_names = [
                "sha256", "api_name", "api_category",
                "call_count", "line_numbers", "context_snippet",
                "revealed_by_deobf",
                "analysis_date",
            ]

            column_type_names = [
                "FixedString(64)", "String", "LowCardinality(String)",
                "UInt32", "Array(UInt32)", "String",
                "UInt8",
                "DateTime64(3, 'UTC')",
            ]

            return (data, column_names, column_type_names)

    def get_clickhouse_table(self) -> str:
        return "redb_js_suspicious_apis"